U.S. patent number 8,537,219 [Application Number 12/407,499] was granted by the patent office on 2013-09-17 for identifying spatial locations of events within video image data.
This patent grant is currently assigned to International Business Machines Corporation. The grantee listed for this patent is Michael J. Desimone, Arun Hampapur, Zuoxuan Lu, Carl P. Mercier, Christopher S. Milite, Stephen R. Russo, Chiao-Fe Shu, Chek K. Tan. Invention is credited to Michael J. Desimone, Arun Hampapur, Zuoxuan Lu, Carl P. Mercier, Christopher S. Milite, Stephen R. Russo, Chiao-Fe Shu, Chek K. Tan.
United States Patent |
8,537,219 |
Desimone , et al. |
September 17, 2013 |
Identifying spatial locations of events within video image data
Abstract
An invention for identifying a spatial location of an event
within video image data is provided. In one embodiment, there is a
spatial representation tool, including a compression component
configured to receive trajectory data of an event within video
image data, and generate a set of compressed spatial representation
of the trajectory data of the event within the video image data. A
database component is configured to input the set of compressed
spatial representations into a relational database, and a search
component is configured to search the relational database to
identify a spatial location of the event within the video image
data.
Inventors: |
Desimone; Michael J.
(Ridgefield, CT), Hampapur; Arun (Norwalk, CT), Lu;
Zuoxuan (Yorktown Heights, NY), Mercier; Carl P. (New
Milford, CT), Milite; Christopher S. (Oxford, CT), Russo;
Stephen R. (Southbury, CT), Shu; Chiao-Fe (Scarsdale,
NY), Tan; Chek K. (Danbury, CT) |
Applicant: |
Name |
City |
State |
Country |
Type |
Desimone; Michael J.
Hampapur; Arun
Lu; Zuoxuan
Mercier; Carl P.
Milite; Christopher S.
Russo; Stephen R.
Shu; Chiao-Fe
Tan; Chek K. |
Ridgefield
Norwalk
Yorktown Heights
New Milford
Oxford
Southbury
Scarsdale
Danbury |
CT
CT
NY
CT
CT
CT
NY
CT |
US
US
US
US
US
US
US
US |
|
|
Assignee: |
International Business Machines
Corporation (Armonk, NY)
|
Family
ID: |
42272106 |
Appl.
No.: |
12/407,499 |
Filed: |
March 19, 2009 |
Prior Publication Data
|
|
|
|
Document
Identifier |
Publication Date |
|
US 20100238285 A1 |
Sep 23, 2010 |
|
Current U.S.
Class: |
348/143; 348/145;
348/146; 348/144 |
Current CPC
Class: |
G06F
16/7328 (20190101); G06K 9/48 (20130101); H04N
7/18 (20130101); G06F 16/532 (20190101); G06K
9/00771 (20130101); G06K 9/00335 (20130101); G06K
9/00711 (20130101); G06T 7/97 (20170101); G06F
16/785 (20190101); G06F 16/444 (20190101); G06K
2009/00738 (20130101); G06T 2207/30232 (20130101); G06T
2207/30241 (20130101) |
Current International
Class: |
H04N
7/18 (20060101) |
References Cited
[Referenced By]
U.S. Patent Documents
Other References
Ziliani, F. et al., "Effective integration of object tracking in a
video coding scheme for multisensor surveillance systems,"
Proceedings of the 2002 International Conference on Image
Processing, Rochester, New York, Sep. 22-25, pp. 521-524. cited by
applicant .
Dimitrova, Nevenka and Golshani, Forouzan, "Motion Recovery for
Video Content Classification", ACM Transactions on Information
Systems, vol. 13, No. 4, Oct. 1995, pp. 408-439. cited by applicant
.
Tian, Ying-li, et al, "Event Detection, Query, and Retrieval for
Video Surveillance", Artificial Intelligence for Maximizing Content
Based Image Retrieval, Chapter XV, pp. 342-370. cited by applicant
.
Luciano da Fontoura Costa and Roberto Marcondes Cesar Jr., "Shape
Analysis and Classification", CRC Press, 2001. cited by applicant
.
Maytham H. Safar and Cyrus Shahabi, "Shape Analysis and Retrieval
of Multimedia Objects", Kluwer Academic Publishers, 2003. cited by
applicant .
Communication Relating to the Results of the Partial International
Search, PCT/EP2010/052636, mailed Sep. 14, 2010. cited by applicant
.
International Search Report, PCT/EP2010/052636, mailed Dec. 29,
2010. cited by applicant.
|
Primary Examiner: Bayard; Djenane
Attorney, Agent or Firm: Schiesser; William E. Keohane &
D'Alessandro, PLLC
Claims
What is claimed is:
1. A method for identifying a spatial location of an event within
video image data comprising: receiving trajectory data of a
trajectory of an object for an event within video image data;
generating a set of compressed spatial representations of the
trajectory data of the trajectory of the object for the event;
generating a lossless contour code of the trajectory data of the
trajectory of the object for the event; inputting each of the set
of compressed spatial representations of the trajectory data of the
trajectory of the object for the event and the lossless contour
code of the trajectory data of the trajectory of the object for the
event into a relational database; and searching the relational
database to identify a spatial location of the event within the
video image data.
2. The method according to claim 1, the generating comprising
generating a searchable code of the trajectory data of the event
within the video image data.
3. The method according to claim 2, the generating comprising
generating a compressed contour-coded blob of the trajectory data
of the event within the video image data.
4. The method according to claim 3, the inputting comprising
inputting the searchable code of the trajectory data of the event
within the video image data and the compressed contour-coded blob
of the trajectory data of the event within the video image into the
relational database.
5. The method according to claim 3, the searching comprising:
specifying a region of interest within the video image data;
converting the region of interest to a lossy query code; and
comparing the lossy query code to the searchable code of the
trajectory data of the event within the video image data.
6. The method according to claim 5, further comprising:
decompressing the contour-coded blob corresponding to the lossy
query code based on the comparing; and plotting a trajectory of the
event within the video image data.
7. A system for identifying a spatial location of an event within
video image data comprising: at least one processing unit; memory
operably associated with the at least one processing unit; and a
spatial representation tool storable in memory and executable by
the at least one processing unit, the spatial representation tool
comprising: a compression component configured to: receive
trajectory data of a trajectory of an object for an event within
video image data; generate a set of compressed spatial
representations of the trajectory data of the trajectory of the
object for the event; and generate a lossless contour code of the
trajectory data of the trajectory of the object for the event; a
database component configured to input each of the set of
compressed spatial representations of the trajectory data of the
trajectory of the object for the event and the lossless contour
code of the trajectory data of the trajectory of the object for the
event into a relational database; and a search component configured
to search the relational database to identify a spatial location of
the event within the video image data.
8. The spatial representation tool according to claim 7, the
compression component further configured to generate a searchable
code of the trajectory data of the event within the video image
data.
9. The spatial representation tool according to claim 8, the
compression component further configured to generate a compressed
contour-coded blob of the trajectory data of the event within the
video image data.
10. The spatial representation tool according to claim 9, the input
component further configured to input the searchable code of the
trajectory data of the event within the video image data and the
compressed contour-coded blob of the trajectory data of the event
within the video image into the relational database.
11. The spatial representation tool according to claim 9, the
search component further configured to: specify a region of
interest within the video image data; convert the region of
interest to a lossy query code; and compare the lossy query code to
the searchable code of the trajectory data of the event within the
video image data.
12. The spatial representation tool according to claim 11 further
comprising a display component configured to: decompress the
contour-coded blob corresponding to the lossy query code based on
the comparison of the lossy query code to the searchable code of
the trajectory data of the event within the video image data; and
plot a trajectory of the event within the video image data.
13. A computer-readable storage device storing computer
instructions, which when executed, enables a computer system to
identify a spatial location of an event within video image data,
the computer instructions comprising: receiving trajectory data of
a trajectory of an object for an event within video image data;
generating a set of compressed spatial representations of the
trajectory data of the trajectory of the object for the event;
generating a lossless contour code of the trajectory data of the
trajectory of the object for the event; inputting each of the set
of compressed spatial representations of the trajectory data of the
trajectory of the object for the event and the lossless contour
code of the trajectory data of the trajectory of the object for the
event into a relational database; and searching the relational
database to identify a spatial location of the event within the
video image data.
14. The computer-readable storage device according to claim 13, the
computer instructions for generating further comprising generating
a searchable code of the trajectory data of the event within the
video image data.
15. The computer-readable storage device according to claim 14, the
computer instructions for generating further comprising generating
a compressed contour-coded blob of the trajectory data of the event
within the video image data.
16. The computer-readable storage device according to claim 13, the
computer instructions for inputting comprising inputting the
searchable code of the trajectory data of the event within the
video image data and the compressed contour-coded blob of the
trajectory data of the event within the video image into the
relational database.
17. The computer-readable storage device according to claim 15, the
computer instructions for selecting comprising: specifying a region
of interest within the video image data; converting the region of
interest to a lossy query code; and comparing the lossy query code
to the searchable code of the trajectory data of the event within
the video image data.
18. The computer-readable storage device according to claim 17,
further comprising computer instructions for: decompressing the
contour-coded blob corresponding to the lossy query code based on
the comparing; and plotting a trajectory of the event within the
video image data.
19. A method for deploying a spatial representation tool for use in
a computer system that identifies a spatial location of an event
within video image data, the method comprising: providing a
computer infrastructure operable to: receive trajectory data of a
trajectory of an object for an event within video image data;
generate a set of compressed spatial representations of the
trajectory data of the trajectory of the object for the event;
generate a lossless contour code of the trajectory data of the
trajectory of the object for the event; input each of the set of
compressed spatial representations of the trajectory data of the
trajectory of the object for the event and the lossless contour
code of the trajectory data of the trajectory of the object for the
event into a relational database; and search the relational
database to identify a spatial location of the event within the
video image data.
20. The method according to claim 19, the computer infrastructure
operable to generate further operable to generate a searchable code
of the trajectory data of the event within the video image
data.
21. The method according to claim 20, the computer infrastructure
operable to generate further operable to generate a compressed
contour-coded blob of the trajectory data of the event within the
video image data.
22. The method according to claim 20, the computer infrastructure
operable to input operable to input the searchable code of the
trajectory data of the event within the video image data and the
compressed contour-coded blob of the trajectory data of the event
within the video image into the relational database.
23. The method according to claim 21, the computer infrastructure
operable to search operable to: specify a region of interest within
the video image data; convert the region of interest to a lossy
query code; and compare the lossy query code to the searchable code
of the trajectory data of the event within the video image
data.
24. The method according to claim 23, the computer infrastructure
further operable to: decompress the contour-coded blob
corresponding to the lossy query code based on the comparison of
the lossy query code to the searchable code of the trajectory data
of the event within the video image data; and plot a trajectory of
the event within the video image data.
Description
CROSS-REFERENCE TO RELATED APPLICATION
This application is related in some aspects to the commonly owned
and co-pending application entitled "Coding Scheme for Identifying
Locations of Events Within Video Image Data," filed Mar. 19, 2009,
and which is assigned U.S. patent application Ser.No.
12/407,520.
FIELD OF THE INVENTION
The present invention generally relates to video surveillance, and
more specifically to spatial surveillance event searching.
BACKGROUND OF THE INVENTION
Large surveillance networks that are deployed on buildings,
highways, trains, metro stations, etc., integrate a large number of
cameras, sensors, and information. Human operators typically cannot
adequately control and monitor all the cameras within a large
surveillance system. As such, many prior art approaches involve
object detection and tracking techniques to identify and analyze
events occurring within a camera field of view. However, when it
comes to searching through large amounts of video data in an effort
to identify an event within video image data, it is difficult to
obtain reliable results.
For example, consider a surveillance camera that is monitoring a
long-term parking lot. The parking lot attendant receives a
complaint that a car has been vandalized at some point in the past
month. The prior art requires either a manual review of tapes/files
from the video camera for the entire month, or the use of a query
box drawn around the particular parking spot with the surveillance
system retrieving all movement that occurred in the query box. The
first approach is typically ineffective because an operator or
group of operators must review hundreds of hours of video to
observe an event that may have lasted a few seconds. The second
approach uses automatic video object tracking and meta-data
indexing using a standard relational database to support spatial
queries. However, the drawback of this approach is that the
representation of the meta-data is very voluminous and makes the
indexing of large numbers of cameras impractical due to the heavy
volume of network traffic and the size of database tables
created.
SUMMARY OF THE INVENTION
In one embodiment, there is a method for identifying a spatial
location of an event within video image data. In this embodiment,
the method comprises: receiving trajectory data of an event within
video image data; generating a set of compressed spatial
representations of the trajectory data of the event within the
video image data; inputting the set of compressed spatial
representations into a relational database; and searching the
relational database to identify a spatial location of the event
within the video image data.
In a second embodiment, there is a system for identifying a spatial
location of an event within video image data. In this embodiment,
the system comprises at least one processing unit, and memory
operably associated with the at least one processing unit. A
spatial representation tool is storable in memory and executable by
the at least one processing unit. The spatial representation tool
comprises: a compression component configured to receive trajectory
data of an event within video image data, and generate a set of
compressed spatial representation of the trajectory data of the
event within the video image data. A database component is
configured to input the set of compressed spatial representations
into a relational database, and a search component is configured to
search the relational database to identify a spatial location of
the event within the video image data.
In a third embodiment, there is a computer-readable medium storing
computer instructions, which when executed, enables a computer
system to identify a spatial location of an event within video
image data, the computer instructions comprising: receiving
trajectory data of an event within video image data; generating a
set of compressed spatial representations of the trajectory data of
the event within the video image data; inputting the set of
compressed spatial representations into a relational database; and
searching the relational database to identify a spatial location of
the event within the video image data.
In a fourth embodiment, there is a method for deploying a spatial
representation tool for use in a computer system that identifies a
spatial location of an event within video image data. In this
embodiment, a computer infrastructure is provided and is operable
to: receive trajectory data of an event within video image data;
generate a set of compressed spatial representations of the
trajectory data of the event within the video image data; input the
set of compressed spatial representations into a relational
database; and search the relational database to identify a spatial
location of the event within the video image data.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 shows a schematic of an exemplary computing environment in
which elements of the present invention may operate;
FIG. 2 shows a spatial representation tool that operates in the
environment shown in FIG. 1;
FIG. 3 shows a system for searching within video image data
according to embodiments of the invention;
FIG. 4 shows an approach for lossless contour coding generation
according to embodiments of the invention;
FIG. 5 shows an approach for lossy search code generation according
to embodiments of the invention;
FIG. 6 shows an approach for identifying an event within the video
image data according to embodiments of the invention; and
FIG. 7 shows a flow diagram of a method for searching within the
video image data according to embodiments of the invention.
The drawings are not necessarily to scale. The drawings are merely
schematic representations, not intended to portray specific
parameters of the invention. The drawings are intended to depict
only typical embodiments of the invention, and therefore should not
be considered as limiting the scope of the invention. In the
drawings, like numbering represents like elements.
DETAILED DESCRIPTION OF THE INVENTION
Embodiments of this invention are directed to searching large
numbers of surveillance camera events using relational database
tables based on the location of an event within a camera field of
view. In these embodiments, a spatial representation tool provides
this capability. Specifically, the spatial representation tool
comprises a compression component configured to receive trajectory
data of an event within video image data, and generate a set of
compressed spatial representation of the trajectory data of the
event within the video image data. A database component is
configured to input the set of compressed spatial representations
into a relational database, and a search component is configured to
search the relational database to identify a spatial location of
the event within the video image data.
FIG. 1 illustrates a computerized implementation 100 of the present
invention. As depicted, implementation 100 includes computer system
104 deployed within a computer infrastructure 102. This is intended
to demonstrate, among other things, that the present invention
could be implemented within a network environment (e.g., the
Internet, a wide area network (WAN), a local area network (LAN), a
virtual private network (VPN), etc.), or on a stand-alone computer
system. In the case of the former, communication throughout the
network can occur via any combination of various types of
communications links. For example, the communication links can
comprise addressable connections that may utilize any combination
of wired and/or wireless transmission methods. Where communications
occur via the Internet, connectivity could be provided by
conventional TCP/IP sockets-based protocol, and an Internet service
provider could be used to establish connectivity to the Internet.
Still yet, computer infrastructure 102 is intended to demonstrate
that some or all of the components of implementation 100 could be
deployed, managed, serviced, etc., by a service provider who offers
to implement, deploy, and/or perform the functions of the present
invention for others.
Computer system 104 is intended to represent any type of computer
system that may be implemented in deploying/realizing the teachings
recited herein. In this particular example, computer system 104
represents an illustrative system for generating a coding scheme
for identifying a spatial location of an event within video image
data. It should be understood that any other computers implemented
under the present invention may have different components/software,
but will perform similar functions. As shown, computer system 104
includes a processing unit 106 capable of analyzing sensor data,
and producing a usable output, e.g., compressed video and video
meta-data. Also shown is memory 108 for storing a spatial
representation tool 153, a bus 110, and device interfaces 112.
Computer system 104 is shown communicating with a sensor device 122
that communicates with bus 110 via device interfaces 112. Sensor
device 122 (or multiple sensor devices) includes sensor devices for
capturing image data representing objects and visual attributes of
moving objects (e.g., people, cars, animals, products, etc.) within
a camera view 119 from sensor device 122, including trajectory data
121 and 123 (i.e., paths of events/objects within video image data
119). Sensor device 122 can include virtually any type of sensor
capable of capturing visual attributes of objects, such as, but not
limited to: optical sensors, infrared detectors, thermal cameras,
still cameras, analog video cameras, digital video cameras, or any
other similar device that can generate sensor data of sufficient
quality to support the methods of the invention as described
herein.
Processing unit 106 collects and routes signals representing
outputs from sensor devices 122 to spatial representation tool 153.
The signals can be transmitted over a LAN and/or a WAN (e.g., T1,
T3, 56 kb, X.25), broadband connections (ISDN, Frame Relay, ATM),
wireless links (802.11, Bluetooth, etc.), and so on. In some
embodiments, the video signals may be encrypted using, for example,
trusted key-pair encryption. Different sensor systems may transmit
information using different communication pathways, such as
Ethernet or wireless networks, direct serial or parallel
connections, USB, Firewire.RTM., Bluetooth.RTM., or other
proprietary interfaces. (Firewire is a registered trademark of
Apple Computer, Inc. Bluetooth is a registered trademark of
Bluetooth Special Interest Group (SIG)). In some embodiments,
sensor device 122 is capable of two-way communication, and thus can
receive signals (to power up, to sound an alert, etc.) from spatial
representation tool 153.
In general, processing unit 106 executes computer program code,
such as program code for operating spatial representation tool 153,
which is stored in memory 108 and/or storage system 116. While
executing computer program code, processing unit 106 can read
and/or write data to/from memory 108 and storage system 116 and a
relational database 118. Relational database 118 stores sensor
data, including video metadata generated by processing unit 106, as
well as rules against which the metadata is compared to identify
objects and trajectories of objects present within video image data
119. As will be further described herein, relational database 118
stores trajectory data 117 on each of trajectories 121 and 123,
along with corresponding coding information for efficient querying.
It will be appreciated that storage system 116 and relational
database 118 can include VCRs, DVRs, RAID arrays, USB hard drives,
optical disk recorders, flash storage devices, image analysis
devices, general purpose computers, video enhancement devices,
de-interlacers, scalers, and/or other video or data processing and
storage elements for storing and/or processing video. The video
signals can be captured and stored in various analog and/or digital
formats, including, but not limited to, Nation Television System
Committee (NTSC), Phase Alternating Line (PAL), and Sequential
Color with Memory (SECAM), uncompressed digital signals using DVI
or HDMI connections, and/or compressed digital signals based on a
common codec format (e.g., MPEG, MPEG2, MPEG4, or H.264).
FIG. 2 shows a more detailed view of spatial representation tool
153 according to embodiments of the invention. As shown, spatial
representation tool 153 comprises a compression component 155
configured to receive trajectory data 117 of an event within video
image data 119 (e.g., object and track data from sensor device
122). Compression component 155 processes trajectory data 117 from
sensor device 122 in real-time, identifying objects and
trajectories of objects that are detected in video image data 119.
Compression component 155 provides the software framework for
hosting a wide range of video analytics to accomplish this. The
video analytics are intended to detect and track objects moving
across a field of view and perform an analysis of tracking data
associated with each object. The set of moving objects can be
detected using a number of approaches, including but not limited
to: background modeling, object detection and tracking, spatial
intensity field gradient analysis, diamond search block-based
(DSBB) gradient descent motion estimation, or any other method for
detecting and identifying objects captured by a sensor device.
As shown in FIGS. 2-3, compression component 155 is configured to
receive trajectory data 117 of video image data 119 and generate a
set (i.e., one or more) of compressed spatial representations (132,
134) of trajectory data 117 of the event within video image data
119. In an exemplary embodiment, compression component 155
generates multiple compressed spatial representations of the video
image data 117 using different data compression techniques. For
example, as shown in FIG. 3, both a lossy search code and a
lossless contour code are generated to encode the trajectory of
each event with video image data 119, as will be further described
below. Compression component 155 is configured to generate a
searchable code 132 of trajectory data 117 of the event within
video image data 119, and a compressed contour-coded blob 134 of
trajectory data 117 of the event within video image data 119.
Next, both searchable code 132 and compressed contour-coded blob
134 are stored within relational database 118, along with the
corresponding track ID, for subsequent retrieval. As shown in FIG.
2, spatial representational tool 153 comprises a database component
160 configured to input searchable code 132 of trajectory data 117
of the event within video image data 119 and compressed
contour-coded blob 134 of trajectory data 117 of the event within
video image data 119 into relational database 118. In one
embodiment, database component 160 generates and uploads messages
in extensible mark-up language (XML) to relational database 118
including Track ID, search code represented as a CHAR String, and
contour code packaged as a proprietary file with binary
representation.
During operation, retrieval may occur when a user that is
monitoring video image data 119 wishes to investigate an event
(e.g., a person, a security breach, a criminal act, suspicious
activity, etc.). As shown in FIGS. 2-3, spatial representation tool
153 comprises a search component 165 configured to search
relational database 118 to identify a spatial location of the event
within video image data 119. Specifically, search component 165 is
configured to specify a region of interest 140 (FIG. 3) within
video image data 119. This selection may be performed by the user
monitoring video image data 119, e.g., via a pointing device (not
shown). Search component 165 then converts region of interest 140
to a lossy query code 136 and performs a database search of
relational database 118. Specifically, search component 165
compares lossy query code 136 to searchable code 132 of trajectory
data 117 of the event within video image data 119. In one
embodiment, each row of relational database 118 is evaluated using
a `UDF.fwdarw.C Function` for performing `BITWISE AND` between
lossy query code 136 and searchable code 132 corresponding to each
track in the table. All rows that intersect region of interest 140
are returned as part of the result set to identify the spatial
location of the event.
The result set is then typically returned to the user as a display
148 (e.g., via a graphical user interface). To accomplish this,
spatial representation tool 153 comprises a display component 170
(FIG. 2) configured to decompress contour-coded blob 134
corresponding to lossy query code 136 based on the comparison of
lossy query code 136 to searchable code 132 of trajectory data 117
of the event within video image data 119. Contour-coded blob 134 is
converted back to the original version of trajectory data 117 and
displayed on display 148. Display component 170 plots a trajectory
(147,149) of the event within video image data 119 to identify the
spatial location of the event.
Referring now to FIGS. 3-6, an approach for generating a set of
compressed spatial representations of trajectory data 117 of the
event within video image data 119 will be described in further
detail. As mentioned above, compression component 155 (FIG. 2) is
configured to generate a searchable code 132 of trajectory data 117
of the event within video image data 119, and a compressed
contour-coded blob 134 of trajectory data 117 of the event within
video image data 119. As shown in FIG. 4, in the first case,
compression component 150 is configured to receive track data 117
of event "X" (e.g., a person, a security breach, a criminal act,
suspicious activity, etc.) within video image data 119, and
generate a contour-coded blob 134 from lossless contour code 131
(FIG. 3) to encode trajectory 121 of event "X". To accomplish this,
compression component divides video image data 119 into a plurality
of pixel regions 23A, 23B, 23C, . . . 23N, and determines whether
each of plurality of pixel regions 23A-23N contains track data 117.
That is, each pixel is analyzed to determine if trajectory 121
intersects the pixel. If yes, a `1` is entered into 36 bit
contour-coded blob 134. If trajectory 121 does not intersect the
pixel, `0` is entered. This process is repeated until contour-coded
blob 134 is complete and it is entered into relational database
118.
Next, as shown in FIG. 5, a searchable code 132 of trajectory data
117 of the event within video image data 119 is generated. To
accomplish this, compression component 155 is configured to divide
video image data 119 into a second plurality of pixel regions 25A,
25B, 25C, . . . 25N. As shown, second plurality of pixel regions
25A-25N comprises less pixel regions than plurality of pixel
regions 23A-23N for contour coded blob 134. In this case, the
6.times.6 representation of video image data 119 is quantized into
a 3.times.3 image, thus generating 9 bit searchable code 132. Once
again, to encode track data 117, it is determined whether each of
second plurality of pixel regions 25A-25N contains track data 117.
That is, each pixel is analyzed to determine if trajectory 121
intersects the pixel. If trajectory 121 intersects, a `1` is
entered into 9 bit searchable code 132. If trajectory 121 does not
intersect the pixel, a `0` is entered. This process is repeated
until searchable code 132 is formed, and searchable code 132 is
then entered into relational database 118 to enable rapid
searching.
Next, as shown in FIG. 6, trajectory data 117 of trajectory 121 is
more precisely analyzed. In this embodiment, video image data 119
is analyzed using an 8-point neighborhood scan 180 to generate the
transition chain code. As shown, event "X" starts at point (0,1),
and the direction of trajectory 121 is plotted according to 8-point
neighborhood scan 180. This embodiment allows increased specificity
over the 6.times.6 image shown in FIG. 4. Rather than simply
identifying whether trajectory 121 is present within each pixel,
8-point neighborhood scan provides information on a direction of
trajectory 121 within each pixel. It will be appreciated that the
precision may be adjusted by increasing or decreasing the number of
points in the neighborhood scan.
It can be appreciated that the methodologies disclosed herein can
be used within a computer system to identify a spatial location of
an event within video image data, as shown in FIG. 1. In this case,
spatial representation tool 153 can be provided, and one or more
systems for performing the processes described in the invention can
be obtained and deployed to computer infrastructure 102. To this
extent, the deployment can comprise one or more of (1) installing
program code on a computing device, such as a computer system, from
a computer-readable medium; (2) adding one or more computing
devices to the infrastructure; and (3) incorporating and/or
modifying one or more existing systems of the infrastructure to
enable the infrastructure to perform the process actions of the
invention.
The exemplary computer system 104 may be described in the general
context of computer-executable instructions, such as program
modules, being executed by a computer. Generally, program modules
include routines, programs, people, components, logic, data
structures, and so on that perform particular tasks or implements
particular abstract data types. Exemplary computer system 104 may
be practiced in distributed computing environments where tasks are
performed by remote processing devices that are linked through a
communications network. In a distributed computing environment,
program modules may be located in both local and remote computer
storage media including memory storage devices.
The program modules carry out the methodologies disclosed herein,
as shown in FIG. 7. According to one embodiment, at 202, trajectory
data of an event within video image data is received. At 204, a set
of compressed spatial representations of the trajectory data of the
event within the video image data is generated. At 204A, a
searchable code of the trajectory data of the event within the
video image is generated. At 204B, a compressed contour-coded blob
of the trajectory data of the event within the video image data is
generated. At 206, the set of compressed spatial representations is
input into a relational database. At 208, the relational database
is searched to identify a spatial location of the event within the
video image data.
The flowchart of FIG. 7 illustrates the architecture,
functionality, and operation of possible implementations of
systems, methods and computer program products according to various
embodiments of the present invention. In this regard, each block in
the flowchart may represent a module, segment, or portion of code,
which comprises one or more executable instructions for
implementing the specified logical function(s). It should also be
noted that, in some alternative implementations, the functions
noted in the blocks may occur out of the order noted in the
figures. For example, two blocks shown in succession may, in fact,
be executed substantially concurrently. It will also be noted that
each block of flowchart illustration can be implemented by special
purpose hardware-based systems that perform the specified functions
or acts, or combinations of special purpose hardware and computer
instructions.
Furthermore, an implementation of exemplary computer system 104
(FIG. 1) may be stored on or transmitted across some form of
computer readable media. Computer readable media can be any
available media that can be accessed by a computer. By way of
example, and not limitation, computer readable media may comprise
"computer storage media" and "communications media."
"Computer storage media" include volatile and non-volatile,
removable and non-removable media implemented in any method or
technology for storage of information such as computer readable
instructions, data structures, program modules, or other data.
Computer storage media includes, but is not limited to, RAM, ROM,
EEPROM, flash memory or other memory technology, CD-ROM, digital
versatile disks (DVD) or other optical storage, magnetic cassettes,
magnetic tape, magnetic disk storage or other magnetic storage
devices, or any other medium which can be used to store the desired
information and which can be accessed by a computer.
"Communication media" typically embodies computer readable
instructions, data structures, program modules, or other data in a
modulated data signal, such as carrier wave or other transport
mechanism. Communication media also includes any information
delivery media.
The term "modulated data signal" means a signal that has one or
more of its characteristics set or changed in such a manner as to
encode information in the signal. By way of example, and not
limitation, communication media includes wired media such as a
wired network or direct-wired connection, and wireless media such
as acoustic, RF, infrared, and other wireless media. Combinations
of any of the above are also included within the scope of computer
readable media.
It is apparent that there has been provided with this invention an
approach for identifying a spatial location of an event within
video image data. While the invention has been particularly shown
and described in conjunction with a preferred embodiment thereof,
it will be appreciated that variations and modifications will occur
to those skilled in the art. Therefore, it is to be understood that
the appended claims are intended to cover all such modifications
and changes that fall within the true spirit of the invention.
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